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From Syllables to Syntax: Investigating Staged Linguistic Development through Computational Modelling

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Kris Jack

This document presents a study on staged language acquisition using computational modeling to identify the relationship between lexical and syntactic development. It explores various strategies and models for language learning, emphasizing the necessity of a unified approach that remains constant across stages and data exposure. The findings suggest that effective language acquisition can be simulated through carefully designed training models and tasks.

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From Syllables to Syntax: Investigating Staged Linguistic Development through Computational Modelling Kris Jack, Chris Reed, and Annalu Waller [kjack|chris|awaller]@computing.dundee.ac.uk Applied Computing, University of Dundee, Dundee, DD1 4HN, Scotland
Staged Language Acquisition • Language acquisition is consistently described in stages • Lexical and syntactic acquisition strategies must operate within a unified model • The Model – Training Data – Initial Assumptions – Lexical Acquisition – Syntactic Acquisition – Comprehension • Results Holophrastic Early Multi- Pre-linguistic Stage Late Multi-word Stage Abstract Stage Stage word Stage 0 months 6 months 12 months 18 months 24 months 30 months 36 months 42 months
Staged Language Acquisition • Language acquisition is consistently described in stages • Lexical and syntactic acquisition strategies must operate within a unified model • The Model – Training Data – Initial Assumptions – Lexical Acquisition – Syntactic Acquisition – Comprehension • Results Holophrastic Early Multi- Pre-linguistic Stage Late Multi-word Stage Abstract Stage Stage word Stage 0 months 6 months 12 months 18 months 24 months 30 months 36 months 42 months
Lexical Acquisition • Siskind • Steels • Regier Siskind (1996) • Cross-situational analysis – Relationship between the appearance and words and their referents
Lexical Acquisition • Siskind • Steels • Regier Steels (2001) • Language games – Social pressure to communicate within a community of agents can lead to an emergent and shared vocabulary
Lexical Acquisition • Siskind • Steels • Regier Regier (2005) • Associative learning – Fast-mapping – Shape bias • No mechanistic changes – Selective attention
Syntactic Acquisition • Roy • Elman • Kirby Roy (2002) • Trained a grounded robot to play a ‘show-and-tell’ game – Training data were divided into Data 0>t>x simple and complex descriptions x>t>y Data Model y>t>z Data
Syntactic Acquisition • Roy • Elman • Kirby Elman (1993) • Incremental Learning – Mechanistic changes can lead to changes in behaviour t>0 Module t>x Model Data Module t>y Module
Syntactic Acquisition • Roy • Elman • Kirby Kirby (2002) • Iterated Learning – Languages with increasing complexity can emerge across generations of agents Data Data Model Model Model
Question Can we develop a unified model that performs staged language acquisition where: 1. The learning mechanisms are constant AND 2. Exposure to training data is constant?
Bridging the Gap between Words and Syntax • Jack, Reed, and Waller (2004) – Shift from holophrastic to syntactic language – The shift was unrealistic as it appeared very early • A form of substitution was employed (similar to Harris (1966); Wolff (1988); Kirby (2002); van Zaanen (2002)) • If the model encountered A B and A C then B and C were considered substitutable for one another – Given the two rules: » S/eats(john, cake) → johneatscake » S/eats(mary, cake) → maryeatscake – Three rules were derived: » S/eats(x, cake) → N/x eatscake » N/john → john » N/mary → mary • This is a reasonable, yet powerful, form of syntactic learning – The target language was unrealistically simple (two-word sentences)
Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description → → → t=1 t=2 t=3 t=4
Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description a red square has appeared → → → t=1 t=2 t=3 t=4
Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description a pink cross to the upper right of the red circle → → → t=1 t=2 t=3 t=4
Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event socross onsomeone else a blue that can recreate the event based otherthe of the on side description the red circle → → → t=1 t=2 t=3 t=4
Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description another red circle under the pink cross → → → t=1 t=2 t=3 t=4
Training Data • The task was surprisingly complex – Linguistically – Conceptually • An artificial language was constructed based on a simplified problem – Describes the appearance of the second object in a scene – Retained the determiner distinction – Can create sentences such as “a red square a bove the green cir cle” and “a blue tri ang gle to the low er left of the pink star” S = NP1 REL NP2 REL = REL1 | REL2 NP1 = a NP REL1 = a bove | be low | to the REL4 NP2 = the NP REL2 = REL3 REL4 NP = COLOUR SHAPE REL3 = to the low er | to the u pper REL4 = left of | right of COLOUR = black | blue | grey | green | pink | SHAPE = cir cle | cross | dia mond | heart | rec black | red | white tang gle | star | square | tri ang gle
Initial Assumptions • Joint attention is established at around one-year-old (Tomasello, 1995) • Receives <event, description> pairs – An event is a set of six feature tuples – A description is a string {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
Initial Assumptions • Sensitivity to data – Children can identify objects through displacement during motion (Kellman et al., 1987). – Children can use shape and colour to differentiate between objects (e.g. Landau et al., 1988) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
Initial Assumptions • Sensitivity to data – Children show sensitivity to the relative spatial relationships between objects, making distinctions between left and right, and above and below (Quinn, 2003) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
Initial Assumptions • Sensitivity to data – Children can perform analogies (Gentner and Medina, 1998) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
Initial Assumptions • Sensitivity to data – Children can determine transitional probabilities between syllables (Saffran, Aslin, and Newport, 1996) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
The Model • Training the model – The Lexical Analysis Unit • Discovers string-meaning associations – The Syntactic Analysis Unit • Discovers compositional relationships
The Lexical Analysis Unit <event, description> pairs are compared through a form of cross-situational analysis <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
The Lexical Analysis Unit Feature tuple comparisons are value sensitive and object identifier insensitive. Two feature tuples, <v1, (o1)> and <v2, (o2)>, are equivalent iff v1 = v2 <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
The Lexical Analysis Unit Co-occurring syllable sequences are found <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
The Lexical Analysis Unit New <feature tuple set, description> pairs are derived <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle” <{<red, (1)>, <circle, (1)>, <right, (0)>}, “a”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “a”> <{<red, (1)>, <circle, (1)>, <right, (0)>}, “to the”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “red”> <{<red, (1)>, <circle, (1)>, <right, (0)>}, “right of <{<circle, (1)>, <red, (2)>, <right, (0)>}, “to the”> the”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “right of <{<red, (1)>, <circle, (1)>, <right, (0)>}, “red”> the”> <{<red, (1)>, <circle, (1)>, <right, (0)>}, “cir cle”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “cir cle”>
The Lexical Analysis Unit • Cross-situational analysis can produce pairs that share the same strings (homonyms) or the same feature sets (synonyms) • Homonyms and synonyms are removed, following a principle of mutual exclusivity (Markman and Wachtel, 1988) • When pairs are equal, with insensitivity to object identifiers, they are merged. Merging produces a new pair, that expresses both of the relationships <{<red, (1)>}, “red”> is merged with <{<red, (2)>}, “red”> to produce <{<red, (1, 2)>}, “red”>
The Lexical Analysis Unit • From all merged pairs, homonyms are removed by selecting the most probable feature set for each string, Frequency of (Sj | Fi ) P(Fi | Sj ) = Frequency of Sj where Frequency of (Sj | Fi) is the number of times that Sj has been observed with Fi and the Frequency of Sj is the number of times that Sj has been observed • Then synonyms are removed by selecting the most probable string for each feature set, P(Sj | Fi), and erasing the remaining pair’s feature sets • A set of lexical items are derived
The Syntactic Analysis Unit • Compositional relationships are found by combining and comparing lexical items • Lexical items are combined by set union and string concatenation f 1, s1 combined with f 2, s 2 = f 1  f 2, s1 + s 2 • The lexical item triple <<f1, s1>, <f2, s2>, <f3, s3>> expresses a compositional relationship iff <f1, s1> = <f2, s2> combined with <f3, s3>
The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations
The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>>
The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>> <{<red, (1, 2)>, <square, (1, 2)>}, “red square”> <{<red, (1, 2)>}, “red”> <{<square, (1, 2)>}, “square”>
The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>> <{<red, (1, 2)>, <square, (1, 2)>}, “red square”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<square, (1, 2)>}, “square”>
The Syntactic Analysis Unit Rules that modify object identifiers can be constructed <{<red, (2)>}, “a red”> () (<(1, 2) → (2)>) <{}, “a”> <{<red, (1, 2)>}, “red”> <{<blue, (1)>}, “the blue”> () (<(1, 2) → (1)>) <{}, “the”> <{<blue, (1, 2)>}, “blue”>
The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”>
The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”>
The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
Comprehension • The model is tested for evidence of language acquisition through comprehension tasks • The model can comprehend a string by: – Finding it in a phrasal category (lexical) – Or creating it through applying a rule (syntactic)
Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is {<circle, (1, 2)>}
Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule • Find “cir” and “cle” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is found lexically as {<circle, (1, 2)>}
Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule • Find “red” and “dia mond” in phrasal categories of a rule • Find “red dia” and “mond” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} {<red, (1, 2)>}, transformed by (<(1, 2)> → (1, 2)>), and combined with {<diamond, (1, 2)>}, transformed by (<(1, 2)> → (1, 2)>), gives {<red, (1, 2)>, <diamond, (1, 2)>}
Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule • Find “red” and “dia mond” in phrasal categories of a rule • Find “red dia” and “mond” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is found syntactically as {<red, (1, 2)>, <diamond, (1, 2)>}
Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} {<{<pink, (1)>}, “the pink”>, <{<green, (1, 2)>}, “green”>, <{<green, (1)>}, “the green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<white, (1)>}, “the white”>} () {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, (<(1, 2) → (1)>) <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} {<{}, “the”>} {<{<pink, (1, 2)>}, “pink”>, <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>}
Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} Construction Islands <{<pink, (1, 2)>}, “pink”>}
Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>, <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>}
Results • Observing the developmental shift from lexical to syntactic comprehension – Tested for comprehension of colours (10), shapes (10), and colour shape combinations (100) during training. Results are averaged over 10 sessions. Developmental Shift 25 3 3 3 3 8 Pre-linguistic Holophrastic 20 Multi-word Early comprehended No. strings 15 Lexical 10 Syntactic 5 0 0 3 6 9 12 15 18 21 24 27 30 No. <event, description>s entered
Results • Comprehension of colours and shapes compared to colour shape combinations – Tested for lexical comprehension of colours (10), and shapes (10), and syntactic comprehension of colour shape combinations (100) during training. Results are averaged over 10 sessions. Expressivity f 100 % string set 80 comprehended 60 Lexical 40 Syntactic 20 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 No. <event, description>s entered
Conclusions The model demonstrates staged linguistic acquisition – No maturational triggers are employed – Training data are kept constant – Lexical items are required before compositions can be derived
Conclusions The model demonstrates staged linguistic acquisition – No maturational triggers are employed – Training data are kept constant – Lexical items are required before compositions can be derived Can this work be extended into further stage transitions?
Thank you

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From Syllables to Syntax: Investigating Staged Linguistic Development through Computational Modelling

  • 1. From Syllables to Syntax: Investigating Staged Linguistic Development through Computational Modelling Kris Jack, Chris Reed, and Annalu Waller [kjack|chris|awaller]@computing.dundee.ac.uk Applied Computing, University of Dundee, Dundee, DD1 4HN, Scotland
  • 2. Staged Language Acquisition • Language acquisition is consistently described in stages • Lexical and syntactic acquisition strategies must operate within a unified model • The Model – Training Data – Initial Assumptions – Lexical Acquisition – Syntactic Acquisition – Comprehension • Results Holophrastic Early Multi- Pre-linguistic Stage Late Multi-word Stage Abstract Stage Stage word Stage 0 months 6 months 12 months 18 months 24 months 30 months 36 months 42 months
  • 3. Staged Language Acquisition • Language acquisition is consistently described in stages • Lexical and syntactic acquisition strategies must operate within a unified model • The Model – Training Data – Initial Assumptions – Lexical Acquisition – Syntactic Acquisition – Comprehension • Results Holophrastic Early Multi- Pre-linguistic Stage Late Multi-word Stage Abstract Stage Stage word Stage 0 months 6 months 12 months 18 months 24 months 30 months 36 months 42 months
  • 4. Lexical Acquisition • Siskind • Steels • Regier Siskind (1996) • Cross-situational analysis – Relationship between the appearance and words and their referents
  • 5. Lexical Acquisition • Siskind • Steels • Regier Steels (2001) • Language games – Social pressure to communicate within a community of agents can lead to an emergent and shared vocabulary
  • 6. Lexical Acquisition • Siskind • Steels • Regier Regier (2005) • Associative learning – Fast-mapping – Shape bias • No mechanistic changes – Selective attention
  • 7. Syntactic Acquisition • Roy • Elman • Kirby Roy (2002) • Trained a grounded robot to play a ‘show-and-tell’ game – Training data were divided into Data 0>t>x simple and complex descriptions x>t>y Data Model y>t>z Data
  • 8. Syntactic Acquisition • Roy • Elman • Kirby Elman (1993) • Incremental Learning – Mechanistic changes can lead to changes in behaviour t>0 Module t>x Model Data Module t>y Module
  • 9. Syntactic Acquisition • Roy • Elman • Kirby Kirby (2002) • Iterated Learning – Languages with increasing complexity can emerge across generations of agents Data Data Model Model Model
  • 10. Question Can we develop a unified model that performs staged language acquisition where: 1. The learning mechanisms are constant AND 2. Exposure to training data is constant?
  • 11. Bridging the Gap between Words and Syntax • Jack, Reed, and Waller (2004) – Shift from holophrastic to syntactic language – The shift was unrealistic as it appeared very early • A form of substitution was employed (similar to Harris (1966); Wolff (1988); Kirby (2002); van Zaanen (2002)) • If the model encountered A B and A C then B and C were considered substitutable for one another – Given the two rules: » S/eats(john, cake) → johneatscake » S/eats(mary, cake) → maryeatscake – Three rules were derived: » S/eats(x, cake) → N/x eatscake » N/john → john » N/mary → mary • This is a reasonable, yet powerful, form of syntactic learning – The target language was unrealistically simple (two-word sentences)
  • 12. Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description → → → t=1 t=2 t=3 t=4
  • 13. Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description a red square has appeared → → → t=1 t=2 t=3 t=4
  • 14. Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description a pink cross to the upper right of the red circle → → → t=1 t=2 t=3 t=4
  • 15. Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event socross onsomeone else a blue that can recreate the event based otherthe of the on side description the red circle → → → t=1 t=2 t=3 t=4
  • 16. Training Data • Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description another red circle under the pink cross → → → t=1 t=2 t=3 t=4
  • 17. Training Data • The task was surprisingly complex – Linguistically – Conceptually • An artificial language was constructed based on a simplified problem – Describes the appearance of the second object in a scene – Retained the determiner distinction – Can create sentences such as “a red square a bove the green cir cle” and “a blue tri ang gle to the low er left of the pink star” S = NP1 REL NP2 REL = REL1 | REL2 NP1 = a NP REL1 = a bove | be low | to the REL4 NP2 = the NP REL2 = REL3 REL4 NP = COLOUR SHAPE REL3 = to the low er | to the u pper REL4 = left of | right of COLOUR = black | blue | grey | green | pink | SHAPE = cir cle | cross | dia mond | heart | rec black | red | white tang gle | star | square | tri ang gle
  • 18. Initial Assumptions • Joint attention is established at around one-year-old (Tomasello, 1995) • Receives <event, description> pairs – An event is a set of six feature tuples – A description is a string {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  • 19. Initial Assumptions • Sensitivity to data – Children can identify objects through displacement during motion (Kellman et al., 1987). – Children can use shape and colour to differentiate between objects (e.g. Landau et al., 1988) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  • 20. Initial Assumptions • Sensitivity to data – Children show sensitivity to the relative spatial relationships between objects, making distinctions between left and right, and above and below (Quinn, 2003) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  • 21. Initial Assumptions • Sensitivity to data – Children can perform analogies (Gentner and Medina, 1998) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  • 22. Initial Assumptions • Sensitivity to data – Children can determine transitional probabilities between syllables (Saffran, Aslin, and Newport, 1996) {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  • 23. The Model • Training the model – The Lexical Analysis Unit • Discovers string-meaning associations – The Syntactic Analysis Unit • Discovers compositional relationships
  • 24. The Lexical Analysis Unit <event, description> pairs are compared through a form of cross-situational analysis <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
  • 25. The Lexical Analysis Unit Feature tuple comparisons are value sensitive and object identifier insensitive. Two feature tuples, <v1, (o1)> and <v2, (o2)>, are equivalent iff v1 = v2 <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
  • 26. The Lexical Analysis Unit Co-occurring syllable sequences are found <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
  • 27. The Lexical Analysis Unit New <feature tuple set, description> pairs are derived <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle” <{<red, (1)>, <circle, (1)>, <right, (0)>}, “a”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “a”> <{<red, (1)>, <circle, (1)>, <right, (0)>}, “to the”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “red”> <{<red, (1)>, <circle, (1)>, <right, (0)>}, “right of <{<circle, (1)>, <red, (2)>, <right, (0)>}, “to the”> the”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “right of <{<red, (1)>, <circle, (1)>, <right, (0)>}, “red”> the”> <{<red, (1)>, <circle, (1)>, <right, (0)>}, “cir cle”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “cir cle”>
  • 28. The Lexical Analysis Unit • Cross-situational analysis can produce pairs that share the same strings (homonyms) or the same feature sets (synonyms) • Homonyms and synonyms are removed, following a principle of mutual exclusivity (Markman and Wachtel, 1988) • When pairs are equal, with insensitivity to object identifiers, they are merged. Merging produces a new pair, that expresses both of the relationships <{<red, (1)>}, “red”> is merged with <{<red, (2)>}, “red”> to produce <{<red, (1, 2)>}, “red”>
  • 29. The Lexical Analysis Unit • From all merged pairs, homonyms are removed by selecting the most probable feature set for each string, Frequency of (Sj | Fi ) P(Fi | Sj ) = Frequency of Sj where Frequency of (Sj | Fi) is the number of times that Sj has been observed with Fi and the Frequency of Sj is the number of times that Sj has been observed • Then synonyms are removed by selecting the most probable string for each feature set, P(Sj | Fi), and erasing the remaining pair’s feature sets • A set of lexical items are derived
  • 30. The Syntactic Analysis Unit • Compositional relationships are found by combining and comparing lexical items • Lexical items are combined by set union and string concatenation f 1, s1 combined with f 2, s 2 = f 1  f 2, s1 + s 2 • The lexical item triple <<f1, s1>, <f2, s2>, <f3, s3>> expresses a compositional relationship iff <f1, s1> = <f2, s2> combined with <f3, s3>
  • 31. The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations
  • 32. The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>>
  • 33. The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>> <{<red, (1, 2)>, <square, (1, 2)>}, “red square”> <{<red, (1, 2)>}, “red”> <{<square, (1, 2)>}, “square”>
  • 34. The Syntactic Analysis Unit A lexical item triple can be made to express a rule by: 1. Converting lexical items into phrasal categories 2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>> <{<red, (1, 2)>, <square, (1, 2)>}, “red square”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<square, (1, 2)>}, “square”>
  • 35. The Syntactic Analysis Unit Rules that modify object identifiers can be constructed <{<red, (2)>}, “a red”> () (<(1, 2) → (2)>) <{}, “a”> <{<red, (1, 2)>}, “red”> <{<blue, (1)>}, “the blue”> () (<(1, 2) → (1)>) <{}, “the”> <{<blue, (1, 2)>}, “blue”>
  • 36. The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”>
  • 37. The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”>
  • 38. The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  • 39. Comprehension • The model is tested for evidence of language acquisition through comprehension tasks • The model can comprehend a string by: – Finding it in a phrasal category (lexical) – Or creating it through applying a rule (syntactic)
  • 40. Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  • 41. Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is {<circle, (1, 2)>}
  • 42. Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  • 43. Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule • Find “cir” and “cle” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
  • 44. Comprehension • Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is found lexically as {<circle, (1, 2)>}
  • 45. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  • 46. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
  • 47. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  • 48. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule • Find “red” and “dia mond” in phrasal categories of a rule • Find “red dia” and “mond” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} {<red, (1, 2)>}, transformed by (<(1, 2)> → (1, 2)>), and combined with {<diamond, (1, 2)>}, transformed by (<(1, 2)> → (1, 2)>), gives {<red, (1, 2)>, <diamond, (1, 2)>}
  • 49. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule • Find “red” and “dia mond” in phrasal categories of a rule • Find “red dia” and “mond” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
  • 50. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is found syntactically as {<red, (1, 2)>, <diamond, (1, 2)>}
  • 51. Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} {<{<pink, (1)>}, “the pink”>, <{<green, (1, 2)>}, “green”>, <{<green, (1)>}, “the green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<white, (1)>}, “the white”>} () {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, (<(1, 2) → (1)>) <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} {<{}, “the”>} {<{<pink, (1, 2)>}, “pink”>, <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>}
  • 52. Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} Construction Islands <{<pink, (1, 2)>}, “pink”>}
  • 53. Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>, <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>}
  • 54. Results • Observing the developmental shift from lexical to syntactic comprehension – Tested for comprehension of colours (10), shapes (10), and colour shape combinations (100) during training. Results are averaged over 10 sessions. Developmental Shift 25 3 3 3 3 8 Pre-linguistic Holophrastic 20 Multi-word Early comprehended No. strings 15 Lexical 10 Syntactic 5 0 0 3 6 9 12 15 18 21 24 27 30 No. <event, description>s entered
  • 55. Results • Comprehension of colours and shapes compared to colour shape combinations – Tested for lexical comprehension of colours (10), and shapes (10), and syntactic comprehension of colour shape combinations (100) during training. Results are averaged over 10 sessions. Expressivity f 100 % string set 80 comprehended 60 Lexical 40 Syntactic 20 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 No. <event, description>s entered
  • 56. Conclusions The model demonstrates staged linguistic acquisition – No maturational triggers are employed – Training data are kept constant – Lexical items are required before compositions can be derived
  • 57. Conclusions The model demonstrates staged linguistic acquisition – No maturational triggers are employed – Training data are kept constant – Lexical items are required before compositions can be derived Can this work be extended into further stage transitions?